HIGH-DIMENSIONAL MULTIVARIATE MEDIATION: WITH APPLICATION TO NEUROIMAGING DATA
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Date
2016-08-22
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Johns Hopkins University
Abstract
Mediation analysis is an important tool in the behavioral sciences for investigating the role of intermediate variables that lie in the path between a randomized treatment and an outcome variable. The influence of the intermediate variable on the outcome is often explored using a linear structural equation models (LSEM), with model coefficients interpreted as possible effects. While there has been significant research on the topic in recent years, little work has been done when the intermediate variable (mediator) is a high-dimensional vector. In this work we introduce a novel method for performing mediation analysis in this setting called the directions of mediation (DMs). DM's linearly combine mediators into a smaller number of orthogonal components, with components ranked by the proportion of the LSEM likelihood (assuming normally distributed errors) each accounts for. This method is well suited for cases when many potential mediators are measured. Examples of high-dimensional potential mediators are brain images composed of hundreds of thousands of voxels, genetic variation measured at millions of SNPs, or vectors of thousands of variables in large-scale epidemiological studies. We demonstrate the method using a functional magnetic resonance imaging (fMRI) study of thermal pain where we are interested in determining which brain locations mediate the relationship between the application of a thermal stimulus and self-reported pain.
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Keywords
Directions of mediation, principal components analysis, fMRI, mediation analysis, structural equation models, high-dimensional data